Mapbox Workshop

R Markdown

Grabbing Static tiles

There are various styles, including streets, outdoors, light, dark, satellite, satellite stretes, navigation day, navigation night. https://docs.mapbox.com/api/maps/styles/

mapbox_token <- Sys.getenv("MAPBOX_PUBLIC_TOKEN")

ny_tracts <- tracts("NY",c("Kings","Bronx","Queens","Richmond","New York"),cb = TRUE)

## Retrieving data for the year 2021

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NYC_Income <- get_acs(
  geography = "tract",
  variables = "B19013_001",
  state = "NY",
  county = c("Kings","Bronx","Queens","Richmond","New York"),
  geometry = TRUE,
  year = 2019
)

## Getting data from the 2015-2019 5-year ACS

## Warning: • You have not set a Census API key. Users without a key are limited to 500
## queries per day and may experience performance limitations.
## ℹ For best results, get a Census API key at http://api.census.gov/data/
## key_signup.html and then supply the key to the `census_api_key()` function to
## use it throughout your tidycensus session.
## This warning is displayed once per session.

## Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.

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NYC_Income_cleaned  <- NYC_Income %>% 
    drop_na()

NYC_Tiles <- get_static_tiles(
  location = ny_tracts,
  zoom = 9,
  style_id = "light-v9",
  username = "mapbox"
)

## Attribution is required if using Mapbox tiles on a map.
## Add the text '(c) Mapbox, (c) OpenStreetMap' to your map for proper attribution.

tm_shape(NYC_Tiles) + 
  tm_rgb() + 
  tm_shape(NYC_Income_cleaned) + 
  tm_polygons(col = "estimate", 
              alpha = 0.5, palette = "viridis", 
              title = "Median household income\n2015-2019 ACS",
              lwd = .3) + 
  tm_layout(legend.outside = TRUE) + 
  tm_credits("Basemap © Mapbox, © OpenStreetMap", position = c("RIGHT", "BOTTOM"))

Here’s how you can get vector tilesets

vector_extract <- get_vector_tiles(
  tileset_id = "mapbox.mapbox-streets-v8",
  location = c(-73.99405, 40.72033),
  zoom = 15
)

names(vector_extract)

## [1] "building"           "landuse"            "place_label"       
## [4] "poi_label"          "road"               "structure"         
## [7] "transit_stop_label" "water"

tmap_mode("view")

## tmap mode set to interactive viewing

tm_shape(vector_extract$landuse$polygons) + 
  tm_polygons(col = "type", alpha = .4) 

tm_shape(vector_extract$building$polygons) + 
  tm_polygons(col = "type", alpha = .4) 

Plotting with a Mapbox Basemap

# bins <- c(0, 10, 20, 50, 100, 200, 500, 1000, Inf)
# pal <- colorBin("YlOrRd", domain = vector_extract$building$polygons, bins = bins)
factpal <- colorFactor(topo.colors(16), vector_extract$building$polygons$type)

leaflet() %>%
  addMapboxTiles(style_id = "dark-v11",
                 username = "mapbox") %>%
  setView(lng = -73.99405, #c(-73.99405, 40.72033)
          lat = 40.72033,
          zoom = 16) %>% 
    addPolygons(data = vector_extract$building$polygons,
                popup = vector_extract$building$polygons$type,
                color = "white",
                fillColor = ~factpal(type),
                weight = 2) %>% 
    addLegend(pal = factpal,value =  vector_extract$building$polygons$type,opacity = 1)

Isochrone (service area) analysis

isochrones <- mb_isochrone("Chinatown, NYC", 
                           time = c(4, 8, 12),
                           profile = "cycling") 
mapdeck(style = mapdeck_style("dark")) %>%

  add_polygon(data = isochrones, 
              fill_colour = "time",
              fill_opacity = 0.5,
              legend = TRUE) 

## Registered S3 method overwritten by 'jsonify':
##   method     from    
##   print.json jsonlite